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Record W131055011 · doi:10.1177/070674370905400206

An Examination of DSM-IV Borderline Personality Disorder Symptoms and Risk for Death by Suicide: A Psychological Autopsy Study

2009· article· en· W131055011 on OpenAlexaffvenue
Alexander McGirr, Joel Paris, Alain Lesage, Johanne Renaud, Gustavo Turecki

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2009
Typearticle
Languageen
FieldPsychology
TopicPersonality Disorders and Psychopathology
Canadian institutionsUniversité de MontréalMcGill UniversityDouglas Mental Health University InstituteUniversity of Toronto
Fundersnot available
KeywordsBorderline personality disorderPsychologyAutopsyClinical psychologyPsychiatryPoison controlPersonalitySuicide preventionInjury preventionMedicineMedical emergencyPsychoanalysisPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To clarify whether certain Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), borderline personality disorder (BPD) symptoms are more prevalent among people who die by suicide, and thereby better predict suicide risk. METHOD: A psychological autopsy method with best informants was used to investigate DSM-IV BPD symptoms and suicide risk among people who died by suicide and met criteria for BPD (n = 62), and BPD control subjects (n = 35). RESULTS: BPD symptoms in people who died by suicide were less likely to include affective instability and paranoid ideation-dissociative symptoms. The negative association between paranoid ideation-dissociative symptoms and suicide was independent of all other BPD symptoms, Cluster B comorbidity, and alcohol dependence. CONCLUSIONS: We found that discrete DSM-IV BPD symptoms differentiate people with BPD who die by suicide and those who do not. People with BPD who go on to die by suicide appear to constitute a specific subgroup of those who meet criteria for BPD, characterized by different general clinical presentation, but also by different characteristics within BPD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.143
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.335
Teacher spread0.312 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations27
Published2009
Admission routes2
Has abstractyes

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